The P300 component and the visuomotor mental rotation task: context-updating scales to angle of rotation
Bibliographic record
Abstract
The visuomotor mental rotation (VMR) task elicits a reliable increase in response latencies as a function of increasing (and perceptually unfamiliar) angles of rotation (Georgopoulos and Massey 1987: Exp Brain Res; Neely and Heath 2010: Brain Res). Evidence from non-human primates suggests that the increased latencies reflect a monotonic rotation of neural population vectors within frontal motor areas. The present investigation evaluated the behavioural and event-related brain potentials (ERP) associated with the VMR task to determine whether accurate performance is related to a remapping of the environmental parameters of a target or a shift of visual attention from a veridical to a cognitively represented target location. Twenty human participants were provided advanced information to complete a direct (i.e., 0°) or VMR response (35, 75 and 105°) to each of eight concentric targets. Targets were presented for 1,000 ms in advance of response cuing and ERPs were locked to their presentation. Behavioural results indicated that endpoint accuracy and variability increased with increasing angle of rotation. In terms of ERP findings, an early component (i.e., N100) related to the orientating of visuospatial attention did not differ across the VMR tasks. In contrast, the amplitude of a later occurring component (i.e., P300) scaled with increasing angle of rotation, and the amplitude of this component increased with increasing endpoint variability. Importantly, previous research has linked the P300 to a revision of an internal mental model when a mismatch exits between a visual stimulus and a required task goal (i.e., context-updating). As such, we propose that the VMR task is mediated via a top-down and cognitively based reformulation of action space, and that such a process occurs well in advance of response cuing. Moreover, the conjoint behavioural and ERP findings suggest the degree of context-updating decreases the effectiveness of the motor response. Meeting abstract presented at VSS 2013
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".